A Robust Twin Parametric Margin Support Vector Machine for Multiclass Classification
This is an incremental improvement for robust multiclass classification in uncertain data environments.
The paper tackles multiclass classification under feature uncertainty by introducing Twin Parametric Margin Support Vector Machine (TPMSVM) models with robust optimization techniques, showing good performance on real-world datasets.
In this paper, we introduce novel Twin Parametric Margin Support Vector Machine (TPMSVM) models designed to address multiclass classification tasks under feature uncertainty. To handle data perturbations, we construct bounded-by-norm uncertainty set around each training observation and derive the robust counterparts of the deterministic models using robust optimization techniques. To capture complex data structure, we explore both linear and kernel-induced classifiers, providing computationally tractable reformulations of the resulting robust models. Additionally, we propose two alternatives for the final decision function, enhancing models' flexibility. Finally, we validate the effectiveness of the proposed robust multiclass TPMSVM methodology on real-world datasets, showing the good performance of the approach in the presence of uncertainty.